pyjanitor

Python implementation of the R package janitor, and more.

why janitor?

Originally a port of the R package, pyjanitor has evolved from a set of convenient data cleaning routines into an experiment with the method chaining paradigm.

Data preprocessing is best expressed as a directed acyclic graph (DAG) of actions taken on data. We take a base data file as the starting point, and perform actions on it, such as removing null/empty rows, replacing them with other values, adding/renaming/removing columns of data, filtering rows and more.

The pandas API has been invaluable for the Python data science ecosystem, and implements method chaining of a subset of methods as part of the API. For example, resetting indexes (.reset_index()), dropping null values (.dropna()), and more, are accomplished via the appropriate pd.DataFrame method calls.

Inspired by the R statistical language ecosystem, where consistent and good API design in the dplyr package enables end-users, who are not necessarily developers, to concisely express data processing code, I have evolved pyjanitor into a language for expressing the data processing DAG for pandas users.

To accomplish this, actions for which we would need to invoke imperative-style statements, can be replaced with method chains that allow one to read off the logical order of actions taken. Let us see the annotated example below. First, off, here's the textual description of a data cleaning pathway:

1. Create dataframe.
2. Delete one column.
3. Drop rows with empty values in two particular columns.
4. Rename another two columns.
5. Add a new column.

In pandas code, this would look as such:

df = pd.DataFrame(...)  # create a pandas DataFrame somehow.
del df['column1']  # delete a column from the dataframe.
df = df.dropna(subset=['column2', 'column3'])  # drop rows that have empty values in column 2 and 3.
df = df.rename({'column2': 'unicorns', 'column3': 'dragons'})  # rename column2 and column3
df['newcolumn'] = ['iterable', 'of', 'items']  # add a new column.

With pyjanitor, we enable method chaining with method names that are verbs, which describe the action taken.

df = (
    pd.DataFrame(...)
    .remove_columns(['column1'])
    .dropna(subset=['column2', 'column3'])
    .rename_column('column2', 'unicorns')
    .rename_column('column3', 'dragons')
    .add_column('newcolumn', ['iterable', 'of', 'items'])
)

As such, the pyjanitor's etymology has a two-fold relationship to "cleanliness". Firstly, it's about extending Pandas with convenient data cleaning routines. Secondly, it's about providing a cleaner, method-chaining, verb-based API for common pandas routines.

installation

pyjanitor is currently installable from PyPI:

pip install pyjanitor

pyjanitor also can be installed by the conda package manager:

conda install pyjanitor -c conda-forge

functionality

Current functionality includes:

• Cleaning columns name (multi-indexes are possible!)
• Removing empty rows and columns
• Identifying duplicate entries
• Encoding columns as categorical
• Splitting your data into features and targets (for machine learning)
• Adding, removing, and renaming columns
• Coalesce multiple columns into a single column
• Date conversions (from matlab, excel, unix) to Python datetime format.
• Expand a single column that has delimited, categorical values into dummy-encoded variables
• Concatenating and deconcatenating columns, based on a delimiter
• Syntactic sugar for filtering the dataframe based on queries on a column
• Experimental submodules for finance and biology

apis

The idea behind the API is two-fold:

• Copy the R package function names, but enable Pythonic use with method chaining or pandas piping.
• Add other utility functions that make it easy to do data cleaning/preprocessing in pandas.

As such, there are three ways to use the API. The first, and most strongly recommended one, is to use janitor's functions as if they were native to pandas.

import pandas as pd
import janitor  # upon import, functions are registered as part of pandas.

df = pd.DataFrame(...)
df = df.clean_names().remove_empty()  # further method chaining possible.

The second is the functional API.

from janitor import clean_names, remove_empty
import pandas as pd

df = pd.DataFrame(...)
df = clean_names(df)
df = remove_empty(df)

The final way is to use the pipe() method.

from janitor import clean_names, remove_empty
import pandas as pd

df = pd.DataFrame(...)
(df.pipe(clean_names)
   .pipe(remove_empty)
   .pipe(...))

contributing

adding new functionality

Keeping in mind the etymology of pyjanitor, contributing a new function to pyjanitor is a task that is not difficult at all.

define a function

First off, you will need to define the function that expresses the data processing/cleaning routine, such that it accepts a dataframe as the first argument, and returns a modified dataframe:

import pandas_flavor as pf

@pf.register_dataframe_method
def my_data_cleaning_function(df, arg1, arg2, ...):
    # Put data processing function here.
    return df

We use pandas_flavor to register the function natively on a pandas.DataFrame.

add a test case

Secondly, we ask that you contribute an test case, to ensure that it works as intended. This should go inside the tests/test_functions.py file.

feature requests

If you have a feature request, please post it as an issue on the GitHub repository issue tracker. Even better, put in a PR for it! I am more than happy to guide you through the codebase so that you can put in a contribution to the codebase.

Because pyjanitor is currently maintained by volunteers and has no fiscal support, any feature requests will be prioritized according to what maintainers encounter as a need in our day-to-day jobs. Please temper expectations accordingly.

credits

Test data for chemistry submodule can be found at Predictive Toxicology .